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Open Access Thesis
Master of Science (M.S.)
Year Degree Awarded
Month Degree Awarded
Since 1990s, World Health Organization defines abortion as safe if it was done with a recommended method that was appropriate to the pregnancy duration and if the person providing the abortion was trained. In this study, we used a three-tiered categorization on abortion safety. Abortion is less safe if the pregnancy was terminated either by untrained individuals or under dangerous methods, and least safe if neither of the two criteria was met. We included all available empirical data on abortion methods, providers, and settings, and factors affecting safety as covariates to estimate the global, regional, and sub regional distributions of abortion by safety categories for the period 2010-2014.
We applied a Bayesian hierarchical model with two regression submodels to estimate abortion safety. One submodel estimated safe proportions and the other one divided unsafe into less safe and least safe proportions. Country intercepts were included in both submodels and estimated using hierarchical models. Data sources were assigned varying levels of uncertainty or treated as minima or maxima to reflect quality of reporting. We constructed 90% highest density intervals as credible intervals to reflect uncertainty in final outcomes. We carried out model selection using information criteria. We examined model validation and carried out various checks to verify the sensitivity of reporting to prior distributions used and outlying countries. We found that the model was reasonably well calibrated and subregional estimates were not sensitive to outlying observations or prior choice.
Of the 55· 7 million abortions that occurred worldwide each year between 2010–14, we estimated that 30·6 million (54·9%, 90% uncertainty interval 49·9–59·4) were safe, 17·1 million (30·7%, 25·5–35·6) were less safe, and 8·0 million (14·4%, 11·5–18·1) were least safe. The proportion of unsafe abortions was significantly higher in developing countries than developed countries, and significantly higher in countries with highly restrictive abortion laws than in those with less restrictive laws. In-depth assessments of data quality and factors affecting abortion safety in outlying countries may result in further model improvements.
Kang, Zhenning, "Categorizing Abortions By Safety Category: A Bayesian Hierarchical Modeling Approach" (2018). Masters Theses. 651.